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Record W2994627442 · doi:10.1109/iecon.2019.8927101

Open Circuit IGBT Fault Classification using Phase Current in a CHB Converter

2019· article· en· W2994627442 on OpenAlexaff
Ahmed Abuelnaga, Mehdi Narimani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInsulated-gate bipolar transistorShort circuitComputer scienceCapacitorFault (geology)Electronic circuitFault detection and isolationIdentification (biology)Power (physics)Electronic engineeringElectrical engineeringEngineeringVoltageArtificial intelligence

Abstract

fetched live from OpenAlex

Recent field failure reports have showed that power cell faults represent the highest contributor to overall CHB converter failure rates. A big portion of these failures are related to power switches, capacitors, gate driver and control circuit boards. Focusing on power switches faults, they normally fail as short circuit or open circuit. Short circuits are dangerous events, however, there is standard method for fast neutralization. On the other hand, open circuit faults are not as fatal as short circuits. Nonetheless, in some situations, the load could not tolerate current imbalance caused by open circuit faults requiring an effective detection and identification method. In general, IGBT open circuit faults are more difficult to detect, and there is no standard method for detection. As a result, there is an industry need for detection, classification, and identification techniques that can perform effectively under different motor loading conditions. In this paper, a proposed method for classifying cell faults caused by IGBT open circuit is presented. Simulation results are provided in order to evaluate the performance of the proposed method.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.870
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.105
GPT teacher head0.338
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2019
Admission routes1
Has abstractyes

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